Abstract

In this paper, we propose a vision-based simultaneous localization and map-building (SLAM) algorithm using compressed extended Kalman filter (CEKF). SLAM addresses the problem of locating a mobile robot in unknown environments. Extended Kalman filters (EKF) are widely used to solve this problem. However, this filter is very time consuming. To reduce the computational complexity, we apply a CEKF to stereo images while compensating for some of the limitation shown in previous implementations of CEKF. Moreover, we estimate the full DOF, its position and pose, of the mobile robots which is required when operating in the outdoor environment. Outdoor experiments have been conducted to test the effectiveness of the proposed SLAM algorithm.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.